927 lines
32 KiB
Python
927 lines
32 KiB
Python
"""Lab046B: несколько Lab045-кадров в одном непрерывном IQ-потоке.
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Эксперимент программный. Передатчик формирует настоящие Lab045 waveform,
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а приёмник получает только один ``complex64`` массив. Известные TX-границы,
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payload, packet и coded bits не входят в API приёмника. Сравнение с TX
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выполняется отдельно и только после независимого CRC PASS.
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RF TX = 0. Аппаратура не используется.
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"""
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from __future__ import annotations
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import argparse
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import math
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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if __package__ in (None, ""):
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import numpy as np
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from experiments import lab045_hackrf_bpsk_fec as lab045
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from protocol import bpsk_radio as radio
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FRAME_COUNT = 5
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LEADING_BACKGROUND_SAMPLES = 137
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INTER_FRAME_GAPS = (211, 353, 587, 809)
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TRAILING_BACKGROUND_SAMPLES = 233
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IMPAIRED_CFO_HZ = 0.2
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IMPAIRED_PHASE_RAD = 0.7
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IMPAIRED_AMPLITUDE = 0.65
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NOISE_SEED = 46002
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NOISE_SIGMA = 0.17
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IQ_PREAMBLE_THRESHOLD = 0.65
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MAX_FRAMES = 32
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MAX_SEARCH_ITERATIONS = 256
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IMPAIRMENT_BLOCK_SAMPLES = 262_144
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def _amplitude_db(ratio: float) -> float:
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return 20.0 * math.log10(max(float(ratio), 1e-300))
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def _wrap_phase(value: float) -> float:
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return float((value + math.pi) % (2.0 * math.pi) - math.pi)
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def deterministic_payload(frame_index: int) -> bytes:
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"""Создать различимый детерминированный payload одного Lab045-кадра."""
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if frame_index < 0:
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raise ValueError("frame_index не может быть отрицательным")
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random_generator = np.random.Generator(np.random.PCG64(46_000 + frame_index))
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payload = random_generator.integers(
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0,
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256,
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lab045.PAYLOAD_SIZE_BYTES,
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dtype=np.uint8,
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)
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payload[:4] = np.frombuffer(frame_index.to_bytes(4, "big"), dtype=np.uint8)
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return payload.tobytes()
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def build_frame_plans(frame_count: int = FRAME_COUNT) -> tuple[lab045.TransmissionPlan, ...]:
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if frame_count <= 0:
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raise ValueError("нужен хотя бы один кадр")
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return tuple(
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lab045.build_transmission_plan(deterministic_payload(frame_index))
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for frame_index in range(frame_count)
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)
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def malformed_candidate_waveform() -> np.ndarray:
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"""Сформировать настоящий IQ-prefix PRBS7 с заведомо неверным PHY magic."""
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header = lab045.encode_phy_header(lab045.MIN_PACKET_BYTES).copy()
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header[: lab045.HEADER_REPETITION] ^= 1
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bits = np.concatenate((lab045.lab044.preamble_bits(), header))
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shaped = lab045.lab044._scale_and_carrier(radio.bpsk_modulate(bits))
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raw = lab045.lab044.lab043.to_cs8_bytes(shaped)
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return lab045.lab044.signed_iq_bytes_to_complex64(raw)
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@dataclass(frozen=True)
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class ContinuousIqPlan:
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"""TX-side описание; ``frame_starts`` запрещено передавать scanner-у."""
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samples: np.ndarray
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frame_plans: tuple[lab045.TransmissionPlan, ...]
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frame_starts: tuple[int, ...]
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leading_background_samples: int
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inter_frame_gaps: tuple[int, ...]
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trailing_background_samples: int
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cfo_hz: float
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phase_rad: float
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amplitude: float
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noise_sigma: float
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noise_seed: int
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malformed_candidate_start: int | None
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malformed_candidate_sample_count: int
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@property
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def sample_count(self) -> int:
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return int(self.samples.size)
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@property
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def memory_bytes(self) -> int:
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return int(self.samples.nbytes)
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def _apply_impairments_in_place(
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samples: np.ndarray,
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*,
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cfo_hz: float,
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phase_rad: float,
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amplitude: float,
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noise_sigma: float,
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noise_seed: int,
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) -> None:
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"""Применить канал блоками без постоянной full-size complex128 копии."""
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if amplitude <= 0.0 or not np.isfinite(amplitude):
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raise ValueError("amplitude должна быть положительной и конечной")
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if noise_sigma < 0.0 or not np.isfinite(noise_sigma):
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raise ValueError("noise_sigma должна быть неотрицательной и конечной")
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if not np.isfinite(cfo_hz) or not np.isfinite(phase_rad):
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raise ValueError("CFO и phase должны быть конечными")
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phase_step = 2.0 * math.pi * cfo_hz / lab045.SAMPLE_RATE_HZ
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random_generator = np.random.Generator(np.random.PCG64(noise_seed))
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for start in range(0, samples.size, IMPAIRMENT_BLOCK_SAMPLES):
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end = min(samples.size, start + IMPAIRMENT_BLOCK_SAMPLES)
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indexes = np.arange(start, end, dtype=np.float64)
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rotation = np.exp(1j * (phase_rad + phase_step * indexes)).astype(np.complex64)
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samples[start:end] *= np.complex64(amplitude) * rotation
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if noise_sigma > 0.0:
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real_noise = random_generator.standard_normal(end - start, dtype=np.float32)
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imaginary_noise = random_generator.standard_normal(end - start, dtype=np.float32)
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samples.real[start:end] += np.float32(noise_sigma) * real_noise
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samples.imag[start:end] += np.float32(noise_sigma) * imaginary_noise
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def build_continuous_iq_stream(
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frame_plans: tuple[lab045.TransmissionPlan, ...] | list[lab045.TransmissionPlan],
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*,
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leading_background_samples: int = LEADING_BACKGROUND_SAMPLES,
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inter_frame_gaps: tuple[int, ...] | list[int] | None = None,
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trailing_background_samples: int = TRAILING_BACKGROUND_SAMPLES,
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cfo_hz: float = 0.0,
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phase_rad: float = 0.0,
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amplitude: float = 1.0,
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noise_sigma: float = 0.0,
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noise_seed: int = NOISE_SEED,
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prepend_malformed_candidate: bool = False,
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malformed_to_frame_gap_samples: int = 317,
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) -> ContinuousIqPlan:
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"""Собрать один непрерывный ``complex64`` stream из полных Lab045 waveform."""
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plans = tuple(frame_plans)
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if not plans:
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raise ValueError("нужен хотя бы один кадр")
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if leading_background_samples < 0 or trailing_background_samples < 0:
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raise ValueError("фоновые интервалы не могут быть отрицательными")
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if malformed_to_frame_gap_samples < 0:
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raise ValueError("интервал после ложного кандидата не может быть отрицательным")
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if inter_frame_gaps is None:
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gaps = tuple(0 for _ in range(len(plans) - 1))
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else:
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gaps = tuple(int(value) for value in inter_frame_gaps)
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if len(gaps) != len(plans) - 1 or any(value < 0 for value in gaps):
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raise ValueError("нужен один неотрицательный gap между каждой парой кадров")
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malformed = malformed_candidate_waveform() if prepend_malformed_candidate else None
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malformed_count = 0 if malformed is None else int(malformed.size)
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total_samples = (
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leading_background_samples
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+ malformed_count
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+ (malformed_to_frame_gap_samples if malformed is not None else 0)
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+ sum(plan.waveform_sample_count for plan in plans)
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+ sum(gaps)
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+ trailing_background_samples
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)
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samples = np.zeros(total_samples, dtype=np.complex64)
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cursor = leading_background_samples
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malformed_start = None
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if malformed is not None:
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malformed_start = cursor
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samples[cursor : cursor + malformed_count] = malformed
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cursor += malformed_count + malformed_to_frame_gap_samples
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frame_starts: list[int] = []
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for frame_index, plan in enumerate(plans):
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frame_starts.append(cursor)
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waveform = lab045.lab044.signed_iq_bytes_to_complex64(lab045.build_tx_cs8(plan))
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if waveform.dtype != np.complex64 or waveform.size != plan.waveform_sample_count:
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raise ValueError("Lab045 waveform имеет неожиданную форму")
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samples[cursor : cursor + waveform.size] = waveform
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cursor += int(waveform.size)
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if frame_index < len(gaps):
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cursor += gaps[frame_index]
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if cursor + trailing_background_samples != total_samples:
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raise RuntimeError("внутренняя ошибка учёта длины IQ stream")
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_apply_impairments_in_place(
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samples,
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cfo_hz=cfo_hz,
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phase_rad=phase_rad,
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amplitude=amplitude,
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noise_sigma=noise_sigma,
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noise_seed=noise_seed,
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)
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return ContinuousIqPlan(
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samples=samples,
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frame_plans=plans,
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frame_starts=tuple(frame_starts),
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leading_background_samples=leading_background_samples,
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inter_frame_gaps=gaps,
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trailing_background_samples=trailing_background_samples,
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cfo_hz=cfo_hz,
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phase_rad=phase_rad,
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amplitude=amplitude,
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noise_sigma=noise_sigma,
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noise_seed=noise_seed,
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malformed_candidate_start=malformed_start,
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malformed_candidate_sample_count=malformed_count,
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)
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@dataclass(frozen=True)
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class IqPreambleCandidate:
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coarse_start_sample: int
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score: float
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over_median_db: float
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over_second_db: float
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def _decimate_then_downconvert(samples: np.ndarray) -> np.ndarray:
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"""Получить компактный detection stream без full-size baseband копии."""
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decimation = lab045.lab044.lab043.DETECTION_DECIMATION
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decimated = lab045.lab044.lab043.resample_poly(
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samples,
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1,
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decimation,
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window=("kaiser", 8),
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).astype(np.complex64, copy=False)
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decimated -= np.mean(decimated, dtype=np.complex128).astype(np.complex64)
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sample_rate = lab045.SAMPLE_RATE_HZ / decimation
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indexes = np.arange(decimated.size, dtype=np.float64)
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rotation = np.exp(
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-2j * math.pi * lab045.CARRIER_OFFSET_HZ * indexes / sample_rate
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).astype(np.complex64)
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decimated *= rotation
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return decimated
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def _local_candidate_indexes(scores: np.ndarray, threshold: float) -> np.ndarray:
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"""Вернуть все локальные максимумы выше порога без global peak/second gate."""
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values = np.asarray(scores, dtype=np.float32)
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if values.ndim != 1 or values.size == 0:
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return np.empty(0, dtype=np.int64)
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if not 0.0 < threshold <= 1.0:
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raise ValueError("threshold должен лежать в (0, 1]")
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left = np.empty_like(values)
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right = np.empty_like(values)
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left[0] = -np.inf
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left[1:] = values[:-1]
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right[-1] = -np.inf
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right[:-1] = values[1:]
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return np.flatnonzero(
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(values >= np.float32(threshold))
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& (values >= left)
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& (values > right)
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)
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def _detect_iq_preamble_candidates(
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samples: np.ndarray,
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threshold: float,
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) -> tuple[tuple[IqPreambleCandidate, ...], int]:
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reference = lab045.lab044._preamble_reference_samples()
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rx_decimated = _decimate_then_downconvert(samples)
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reference_decimated = _decimate_then_downconvert(reference)
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if rx_decimated.size < reference_decimated.size:
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return (), int(rx_decimated.nbytes + reference_decimated.nbytes)
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scores = np.asarray(
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lab045.lab044.lab043._normalized_sliding_correlation(
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rx_decimated,
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reference_decimated,
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),
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dtype=np.float32,
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)
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scores[~np.isfinite(scores)] = 0.0
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scores = np.clip(scores, 0.0, 1.0)
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indexes = _local_candidate_indexes(scores, threshold)
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median = float(np.median(scores))
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candidate_scores = scores[indexes]
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decimation = lab045.lab044.lab043.DETECTION_DECIMATION
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candidates: list[IqPreambleCandidate] = []
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for position, index in enumerate(indexes):
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if candidate_scores.size > 1:
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others = np.delete(candidate_scores, position)
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second = float(np.max(others))
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else:
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second = 0.0
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score = float(scores[index])
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candidates.append(IqPreambleCandidate(
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coarse_start_sample=int(index) * decimation,
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score=score,
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over_median_db=(
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_amplitude_db(score / median) if median > 0.0 else float("inf")
|
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),
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over_second_db=(
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_amplitude_db(score / second) if second > 0.0 else float("inf")
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),
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))
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storage_bytes = int(
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rx_decimated.nbytes + reference_decimated.nbytes + scores.nbytes
|
||
)
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return tuple(candidates), storage_bytes
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|
||
|
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@dataclass(frozen=True)
|
||
class SoftWindow:
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||
passed: bool
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normalized_real: np.ndarray
|
||
timing_offset_samples: int
|
||
symbol_sample_phase: int
|
||
nominal_sample: int
|
||
carrier: dict
|
||
reason: str | None
|
||
|
||
|
||
def _extract_soft_window(
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samples: np.ndarray,
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||
frame_start: int,
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||
symbol_count: int,
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||
) -> SoftWindow:
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||
waveform_samples = (
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(symbol_count + 2 * lab045.GUARD_SYMBOL_COUNT) * lab045.SAMPLES_PER_SYMBOL
|
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+ lab045.RRC_SPAN_SYMBOLS * lab045.SAMPLES_PER_SYMBOL
|
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)
|
||
if frame_start < 0 or frame_start + waveform_samples > samples.size:
|
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return SoftWindow(False, np.empty(0), 0, 0, 0, {}, "TRUNCATED_IQ_WINDOW")
|
||
segment = samples[frame_start : frame_start + waveform_samples]
|
||
matched, nominal = lab045.lab044.lab043._matched_waveform(
|
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segment,
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0,
|
||
waveform_samples,
|
||
)
|
||
preamble_bits = lab045.lab044.preamble_bits()
|
||
preamble_symbols = radio.bpsk_modulate(preamble_bits)
|
||
timing, _ = lab045.lab044.lab043._timing_offsets(
|
||
matched,
|
||
nominal,
|
||
preamble_symbols,
|
||
)
|
||
carrier = lab045.lab044.lab043._evaluate_symbols(
|
||
matched,
|
||
nominal,
|
||
timing,
|
||
preamble_bits,
|
||
lab045.PREAMBLE_BIT_COUNT,
|
||
)
|
||
if carrier["abs_h"] == 0.0:
|
||
return SoftWindow(False, np.empty(0), timing, 0, nominal, carrier, "ZERO_GAIN")
|
||
symbols = matched[
|
||
nominal + timing : nominal + timing + symbol_count * lab045.SAMPLES_PER_SYMBOL : lab045.SAMPLES_PER_SYMBOL
|
||
].astype(np.complex128)
|
||
if symbols.size != symbol_count:
|
||
return SoftWindow(False, np.empty(0), timing, 0, nominal, carrier, "TRUNCATED_SYMBOLS")
|
||
symbol_time = (np.arange(symbol_count, dtype=np.float64) + 0.5) / lab045.SYMBOL_RATE
|
||
normalized = (
|
||
symbols
|
||
* np.exp(-2j * math.pi * carrier["cfo_hz"] * symbol_time)
|
||
/ (carrier["abs_h"] * np.exp(1j * carrier["phase_rad"]))
|
||
)
|
||
sample_phase = int((frame_start + nominal + timing) % lab045.SAMPLES_PER_SYMBOL)
|
||
return SoftWindow(
|
||
True,
|
||
normalized.real.astype(np.float64, copy=False),
|
||
int(timing),
|
||
sample_phase,
|
||
int(nominal),
|
||
carrier,
|
||
None,
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class IqFrameReception:
|
||
detection_number: int
|
||
coarse_start_sample: int
|
||
refined_start_sample: int
|
||
frame_end_sample: int
|
||
preamble_score: float
|
||
over_median_db: float
|
||
over_second_db: float
|
||
timing_offset_samples: int
|
||
symbol_sample_phase: int
|
||
nominal_sample: int
|
||
estimated_cfo_hz: float
|
||
estimated_phase_rad: float
|
||
bpsk_polarity: str
|
||
preamble_errors: int
|
||
snr_db: float
|
||
post_preamble_soft: np.ndarray
|
||
reception: lab045.IndependentReception
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class CandidateDecode:
|
||
refined_start_sample: int
|
||
next_sample: int
|
||
phy_header_pass: bool
|
||
fec_pass: bool
|
||
crc: str
|
||
accepted: bool
|
||
reason: str
|
||
frame: IqFrameReception | None
|
||
|
||
|
||
def _decode_iq_candidate(
|
||
samples: np.ndarray,
|
||
candidate: IqPreambleCandidate,
|
||
detection_number: int,
|
||
) -> CandidateDecode:
|
||
reference = lab045.lab044._preamble_reference_samples()
|
||
try:
|
||
refined = lab045.lab044.lab043._refine_frame_start(
|
||
samples,
|
||
reference,
|
||
candidate.coarse_start_sample,
|
||
)
|
||
except (ValueError, IndexError):
|
||
return CandidateDecode(
|
||
candidate.coarse_start_sample,
|
||
candidate.coarse_start_sample + 1,
|
||
False,
|
||
False,
|
||
lab045.CRC_NOT_REACHED,
|
||
False,
|
||
"REFINEMENT_FAILED",
|
||
None,
|
||
)
|
||
|
||
probe_symbols = lab045.PREAMBLE_BIT_COUNT + lab045.PHY_HEADER_BIT_COUNT
|
||
probe = _extract_soft_window(samples, refined, probe_symbols)
|
||
if not probe.passed:
|
||
return CandidateDecode(
|
||
refined,
|
||
candidate.coarse_start_sample + 1,
|
||
False,
|
||
False,
|
||
lab045.CRC_NOT_REACHED,
|
||
False,
|
||
probe.reason or "PROBE_FAILED",
|
||
None,
|
||
)
|
||
header = lab045.decode_phy_header_soft(
|
||
probe.normalized_real[
|
||
lab045.PREAMBLE_BIT_COUNT : lab045.PREAMBLE_BIT_COUNT + lab045.PHY_HEADER_BIT_COUNT
|
||
]
|
||
)
|
||
if not header["passed"]:
|
||
return CandidateDecode(
|
||
refined,
|
||
candidate.coarse_start_sample + 1,
|
||
False,
|
||
False,
|
||
lab045.CRC_NOT_REACHED,
|
||
False,
|
||
f"PHY_HEADER_{header['reason']}",
|
||
None,
|
||
)
|
||
|
||
packet_bytes = int(header["packet_bytes"])
|
||
coded_length = lab045.conv.encoded_bit_count(packet_bytes * 8)
|
||
frame_symbols = lab045.PREAMBLE_BIT_COUNT + lab045.PHY_HEADER_BIT_COUNT + coded_length
|
||
full = _extract_soft_window(samples, refined, frame_symbols)
|
||
if not full.passed:
|
||
return CandidateDecode(
|
||
refined,
|
||
candidate.coarse_start_sample + 1,
|
||
True,
|
||
False,
|
||
lab045.CRC_NOT_REACHED,
|
||
False,
|
||
full.reason or "FULL_EXTRACTION_FAILED",
|
||
None,
|
||
)
|
||
post_preamble = full.normalized_real[lab045.PREAMBLE_BIT_COUNT:]
|
||
reception = lab045.analyze_post_preamble_soft(post_preamble)
|
||
crc = str(reception.analysis["crc"])
|
||
accepted = crc == lab045.CRC_PASS
|
||
waveform_samples = (
|
||
(frame_symbols + 2 * lab045.GUARD_SYMBOL_COUNT) * lab045.SAMPLES_PER_SYMBOL
|
||
+ lab045.RRC_SPAN_SYMBOLS * lab045.SAMPLES_PER_SYMBOL
|
||
)
|
||
frame_end = refined + waveform_samples
|
||
preamble_hard = (
|
||
full.normalized_real[: lab045.PREAMBLE_BIT_COUNT] >= 0.0
|
||
).astype(np.uint8)
|
||
preamble_errors = int(np.count_nonzero(
|
||
preamble_hard != lab045.lab044.preamble_bits()
|
||
))
|
||
carrier = full.carrier
|
||
frame = IqFrameReception(
|
||
detection_number=detection_number,
|
||
coarse_start_sample=candidate.coarse_start_sample,
|
||
refined_start_sample=refined,
|
||
frame_end_sample=frame_end,
|
||
preamble_score=candidate.score,
|
||
over_median_db=candidate.over_median_db,
|
||
over_second_db=candidate.over_second_db,
|
||
timing_offset_samples=full.timing_offset_samples,
|
||
symbol_sample_phase=full.symbol_sample_phase,
|
||
nominal_sample=full.nominal_sample,
|
||
estimated_cfo_hz=float(carrier["cfo_hz"]),
|
||
estimated_phase_rad=float(carrier["phase_rad"]),
|
||
bpsk_polarity=str(carrier["polarity"]),
|
||
preamble_errors=preamble_errors,
|
||
snr_db=float(carrier["snr_db"]),
|
||
post_preamble_soft=post_preamble,
|
||
reception=reception,
|
||
)
|
||
reason = "CRC_PASS" if accepted else f"CRC_{crc}"
|
||
return CandidateDecode(
|
||
refined,
|
||
frame_end if accepted else candidate.coarse_start_sample + 1,
|
||
True,
|
||
bool(reception.analysis["fec_decode_pass"]),
|
||
crc,
|
||
accepted,
|
||
reason,
|
||
frame,
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class ContinuousIqReceiveResult:
|
||
iq_sample_count: int
|
||
iq_dtype: str
|
||
preamble_candidates: int
|
||
processed_candidates: int
|
||
rejected_candidates: int
|
||
phy_header_pass_count: int
|
||
fec_pass_count: int
|
||
crc_pass_count: int
|
||
crc_fail_count: int
|
||
accepted_frames: tuple[IqFrameReception, ...]
|
||
rejected_reasons: tuple[str, ...]
|
||
candidate_starts: tuple[int, ...]
|
||
cursor_history: tuple[int, ...]
|
||
search_iterations: int
|
||
termination_reason: str
|
||
max_frames_reached: bool
|
||
max_search_iterations_reached: bool
|
||
detection_storage_bytes: int
|
||
|
||
|
||
def scan_continuous_iq(
|
||
iq_samples: np.ndarray,
|
||
*,
|
||
preamble_threshold: float = IQ_PREAMBLE_THRESHOLD,
|
||
max_frames: int = MAX_FRAMES,
|
||
max_search_iterations: int = MAX_SEARCH_ITERATIONS,
|
||
) -> ContinuousIqReceiveResult:
|
||
"""Найти несколько кадров только по одному IQ stream, без TX offsets."""
|
||
|
||
try:
|
||
samples = np.asarray(iq_samples, dtype=np.complex64)
|
||
except (TypeError, ValueError):
|
||
samples = np.empty((0, 0), dtype=np.complex64)
|
||
if not 0.0 < preamble_threshold <= 1.0:
|
||
raise ValueError("preamble_threshold должен лежать в (0, 1]")
|
||
if max_frames < 0 or max_search_iterations < 0:
|
||
raise ValueError("safety limits не могут быть отрицательными")
|
||
if samples.ndim != 1 or samples.size == 0 or not np.all(np.isfinite(samples)):
|
||
return ContinuousIqReceiveResult(
|
||
int(samples.size),
|
||
str(samples.dtype),
|
||
0,
|
||
0,
|
||
0,
|
||
0,
|
||
0,
|
||
0,
|
||
0,
|
||
(),
|
||
("INVALID_IQ_STREAM",),
|
||
(),
|
||
(0,),
|
||
0,
|
||
"INVALID_IQ_STREAM",
|
||
False,
|
||
False,
|
||
0,
|
||
)
|
||
|
||
candidates, storage_bytes = _detect_iq_preamble_candidates(
|
||
samples,
|
||
preamble_threshold,
|
||
)
|
||
cursor = 0
|
||
cursor_history = [cursor]
|
||
accepted_frames: list[IqFrameReception] = []
|
||
rejected_reasons: list[str] = []
|
||
candidate_starts: list[int] = []
|
||
phy_header_pass = 0
|
||
fec_pass = 0
|
||
crc_pass = 0
|
||
crc_fail = 0
|
||
iterations = 0
|
||
termination = "END_OF_STREAM"
|
||
max_frames_reached = False
|
||
max_iterations_reached = False
|
||
|
||
for candidate in candidates:
|
||
if candidate.coarse_start_sample < cursor:
|
||
continue
|
||
if len(accepted_frames) >= max_frames:
|
||
termination = "MAX_FRAMES_REACHED"
|
||
max_frames_reached = True
|
||
break
|
||
if iterations >= max_search_iterations:
|
||
termination = "MAX_SEARCH_ITERATIONS_REACHED"
|
||
max_iterations_reached = True
|
||
break
|
||
iterations += 1
|
||
candidate_starts.append(candidate.coarse_start_sample)
|
||
decoded = _decode_iq_candidate(samples, candidate, iterations)
|
||
phy_header_pass += int(decoded.phy_header_pass)
|
||
fec_pass += int(decoded.fec_pass)
|
||
crc_pass += int(decoded.crc == lab045.CRC_PASS)
|
||
crc_fail += int(decoded.crc == lab045.CRC_FAIL)
|
||
if decoded.accepted and decoded.frame is not None:
|
||
accepted_frames.append(decoded.frame)
|
||
else:
|
||
rejected_reasons.append(decoded.reason)
|
||
next_cursor = max(cursor + 1, int(decoded.next_sample))
|
||
cursor = next_cursor
|
||
cursor_history.append(cursor)
|
||
|
||
return ContinuousIqReceiveResult(
|
||
iq_sample_count=int(samples.size),
|
||
iq_dtype=str(samples.dtype),
|
||
preamble_candidates=len(candidates),
|
||
processed_candidates=iterations,
|
||
rejected_candidates=len(rejected_reasons),
|
||
phy_header_pass_count=phy_header_pass,
|
||
fec_pass_count=fec_pass,
|
||
crc_pass_count=crc_pass,
|
||
crc_fail_count=crc_fail,
|
||
accepted_frames=tuple(accepted_frames),
|
||
rejected_reasons=tuple(rejected_reasons),
|
||
candidate_starts=tuple(candidate_starts),
|
||
cursor_history=tuple(cursor_history),
|
||
search_iterations=iterations,
|
||
termination_reason=termination,
|
||
max_frames_reached=max_frames_reached,
|
||
max_search_iterations_reached=max_iterations_reached,
|
||
detection_storage_bytes=storage_bytes,
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class FrameDiagnostic:
|
||
detection_number: int
|
||
tx_frame_index: int
|
||
sample_offset: int
|
||
sample_offset_error: int
|
||
symbol_sample_phase: int
|
||
estimated_cfo_hz: float
|
||
estimated_channel_phase_rad: float
|
||
preamble_errors: int
|
||
over_median_db: float
|
||
over_second_db: float
|
||
snr_db: float
|
||
pre_fec_coded_bit_errors: int
|
||
post_fec_protocol_bit_errors: int
|
||
crc: str
|
||
|
||
|
||
def diagnostics_after_crc(
|
||
receive: ContinuousIqReceiveResult,
|
||
stream_plan: ContinuousIqPlan,
|
||
) -> tuple[FrameDiagnostic, ...]:
|
||
"""Сопоставить RX с TX только после независимого CRC PASS."""
|
||
|
||
plan_by_packet = {
|
||
plan.packet: (frame_index, plan)
|
||
for frame_index, plan in enumerate(stream_plan.frame_plans)
|
||
}
|
||
rows: list[FrameDiagnostic] = []
|
||
for frame in receive.accepted_frames:
|
||
packet = frame.reception.packet_bytes
|
||
if packet not in plan_by_packet:
|
||
continue
|
||
frame_index, plan = plan_by_packet[packet]
|
||
comparison = lab045.diagnostic_pre_and_post_fec(
|
||
frame.reception,
|
||
frame.post_preamble_soft,
|
||
plan,
|
||
)
|
||
pre_fec_errors = comparison["pre_fec_coded"]["bit_errors"]
|
||
post_fec_errors = comparison["post_fec_packet"]["bit_errors"]
|
||
if pre_fec_errors is None or post_fec_errors is None:
|
||
raise RuntimeError("CRC PASS frame оказался несопоставим с TX diagnostic")
|
||
phase_reference_sample = (
|
||
frame.refined_start_sample
|
||
+ frame.nominal_sample
|
||
+ frame.timing_offset_samples
|
||
- 0.5 * lab045.SAMPLES_PER_SYMBOL
|
||
)
|
||
estimated_channel_phase = _wrap_phase(
|
||
frame.estimated_phase_rad
|
||
- 2.0
|
||
* math.pi
|
||
* frame.estimated_cfo_hz
|
||
* phase_reference_sample
|
||
/ lab045.SAMPLE_RATE_HZ
|
||
)
|
||
rows.append(FrameDiagnostic(
|
||
detection_number=frame.detection_number,
|
||
tx_frame_index=frame_index,
|
||
sample_offset=frame.refined_start_sample,
|
||
sample_offset_error=(
|
||
frame.refined_start_sample - stream_plan.frame_starts[frame_index]
|
||
),
|
||
symbol_sample_phase=frame.symbol_sample_phase,
|
||
estimated_cfo_hz=frame.estimated_cfo_hz,
|
||
estimated_channel_phase_rad=estimated_channel_phase,
|
||
preamble_errors=frame.preamble_errors,
|
||
over_median_db=frame.over_median_db,
|
||
over_second_db=frame.over_second_db,
|
||
snr_db=frame.snr_db,
|
||
pre_fec_coded_bit_errors=int(pre_fec_errors),
|
||
post_fec_protocol_bit_errors=int(post_fec_errors),
|
||
crc=str(frame.reception.analysis["crc"]),
|
||
))
|
||
return tuple(rows)
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class IqScenarioResult:
|
||
name: str
|
||
frame_count: int
|
||
sample_count: int
|
||
iq_dtype: str
|
||
iq_memory_bytes: int
|
||
frame_starts: tuple[int, ...]
|
||
inter_frame_gaps: tuple[int, ...]
|
||
leading_background_samples: int
|
||
trailing_background_samples: int
|
||
cfo_hz: float
|
||
phase_rad: float
|
||
amplitude: float
|
||
noise_sigma: float
|
||
noise_seed: int
|
||
malformed_candidate_start: int | None
|
||
receive: ContinuousIqReceiveResult
|
||
diagnostics: tuple[FrameDiagnostic, ...]
|
||
|
||
@property
|
||
def pre_fec_coded_bit_errors(self) -> int:
|
||
return sum(row.pre_fec_coded_bit_errors for row in self.diagnostics)
|
||
|
||
@property
|
||
def post_fec_protocol_bit_errors(self) -> int:
|
||
return sum(row.post_fec_protocol_bit_errors for row in self.diagnostics)
|
||
|
||
@property
|
||
def estimated_cfo_hz(self) -> float | None:
|
||
if not self.diagnostics:
|
||
return None
|
||
return float(np.median([row.estimated_cfo_hz for row in self.diagnostics]))
|
||
|
||
@property
|
||
def estimated_phase_rad(self) -> float | None:
|
||
if not self.diagnostics:
|
||
return None
|
||
values = np.asarray(
|
||
[row.estimated_channel_phase_rad for row in self.diagnostics],
|
||
dtype=np.float64,
|
||
)
|
||
return float(np.angle(np.mean(np.exp(1j * values))))
|
||
|
||
|
||
def run_iq_scenario(
|
||
name: str,
|
||
frame_plans: tuple[lab045.TransmissionPlan, ...],
|
||
**stream_arguments,
|
||
) -> IqScenarioResult:
|
||
stream_plan = build_continuous_iq_stream(frame_plans, **stream_arguments)
|
||
receive = scan_continuous_iq(stream_plan.samples)
|
||
diagnostics = diagnostics_after_crc(receive, stream_plan)
|
||
return IqScenarioResult(
|
||
name=name,
|
||
frame_count=len(frame_plans),
|
||
sample_count=stream_plan.sample_count,
|
||
iq_dtype=str(stream_plan.samples.dtype),
|
||
iq_memory_bytes=stream_plan.memory_bytes,
|
||
frame_starts=stream_plan.frame_starts,
|
||
inter_frame_gaps=stream_plan.inter_frame_gaps,
|
||
leading_background_samples=stream_plan.leading_background_samples,
|
||
trailing_background_samples=stream_plan.trailing_background_samples,
|
||
cfo_hz=stream_plan.cfo_hz,
|
||
phase_rad=stream_plan.phase_rad,
|
||
amplitude=stream_plan.amplitude,
|
||
noise_sigma=stream_plan.noise_sigma,
|
||
noise_seed=stream_plan.noise_seed,
|
||
malformed_candidate_start=stream_plan.malformed_candidate_start,
|
||
receive=receive,
|
||
diagnostics=diagnostics,
|
||
)
|
||
|
||
|
||
@dataclass(frozen=True)
|
||
class Lab046bValidation:
|
||
ideal: IqScenarioResult
|
||
impaired: IqScenarioResult
|
||
noisy: IqScenarioResult
|
||
malformed: IqScenarioResult
|
||
|
||
|
||
def run_lab046b_validation(frame_count: int = FRAME_COUNT) -> Lab046bValidation:
|
||
plans = build_frame_plans(frame_count)
|
||
gaps = INTER_FRAME_GAPS[: frame_count - 1]
|
||
if len(gaps) != frame_count - 1:
|
||
gaps = tuple(137 + 2 * index for index in range(frame_count - 1))
|
||
common = {
|
||
"leading_background_samples": LEADING_BACKGROUND_SAMPLES,
|
||
"inter_frame_gaps": gaps,
|
||
"trailing_background_samples": TRAILING_BACKGROUND_SAMPLES,
|
||
}
|
||
ideal = run_iq_scenario("IDEAL", plans, **common)
|
||
impaired = run_iq_scenario(
|
||
"CFO_PHASE_AMPLITUDE",
|
||
plans,
|
||
**common,
|
||
cfo_hz=IMPAIRED_CFO_HZ,
|
||
phase_rad=IMPAIRED_PHASE_RAD,
|
||
amplitude=IMPAIRED_AMPLITUDE,
|
||
)
|
||
noisy = run_iq_scenario(
|
||
"COMPLEX_GAUSSIAN_NOISE",
|
||
plans,
|
||
**common,
|
||
noise_sigma=NOISE_SIGMA,
|
||
noise_seed=NOISE_SEED,
|
||
)
|
||
malformed = run_iq_scenario(
|
||
"MALFORMED_CANDIDATE",
|
||
plans[:1],
|
||
leading_background_samples=LEADING_BACKGROUND_SAMPLES,
|
||
inter_frame_gaps=(),
|
||
trailing_background_samples=TRAILING_BACKGROUND_SAMPLES,
|
||
prepend_malformed_candidate=True,
|
||
)
|
||
return Lab046bValidation(ideal, impaired, noisy, malformed)
|
||
|
||
|
||
def run_functional_tests() -> None:
|
||
scores = np.asarray([0.0, 0.8, 0.1, 0.9, 0.0], dtype=np.float32)
|
||
assert _local_candidate_indexes(scores, 0.65).tolist() == [1, 3]
|
||
assert scan_continuous_iq(np.empty(0, dtype=np.complex64)).termination_reason == "INVALID_IQ_STREAM"
|
||
|
||
|
||
def _print_scenario(result: IqScenarioResult) -> None:
|
||
print(f"{result.name}:")
|
||
print(f" samples = {result.sample_count}")
|
||
print(f" dtype = {result.iq_dtype}")
|
||
print(f" preamble candidates = {result.receive.preamble_candidates}")
|
||
print(f" processed candidates = {result.receive.processed_candidates}")
|
||
print(f" accepted frames = {len(result.receive.accepted_frames)}")
|
||
print(f" rejected candidates = {result.receive.rejected_candidates}")
|
||
print(f" PHY header PASS = {result.receive.phy_header_pass_count}")
|
||
print(f" FEC PASS = {result.receive.fec_pass_count}")
|
||
print(f" CRC PASS/FAIL = {result.receive.crc_pass_count}/{result.receive.crc_fail_count}")
|
||
print(f" pre/post-FEC bit errors = {result.pre_fec_coded_bit_errors}/{result.post_fec_protocol_bit_errors}")
|
||
print(f" estimated CFO = {result.estimated_cfo_hz}")
|
||
print(f" estimated phase = {result.estimated_phase_rad}")
|
||
for row in result.diagnostics:
|
||
print(
|
||
" frame "
|
||
f"{row.tx_frame_index}: offset={row.sample_offset}, "
|
||
f"offset_error={row.sample_offset_error}, "
|
||
f"sample_phase={row.symbol_sample_phase}, "
|
||
f"CFO={row.estimated_cfo_hz:.9f} Hz, "
|
||
f"phase={row.estimated_channel_phase_rad:.9f} rad, "
|
||
f"preamble_errors={row.preamble_errors}, "
|
||
f"peak/median={row.over_median_db:.3f} dB, "
|
||
f"peak/second={row.over_second_db:.3f} dB, "
|
||
f"SNR={row.snr_db:.3f} dB, "
|
||
f"pre/post-FEC={row.pre_fec_coded_bit_errors}/"
|
||
f"{row.post_fec_protocol_bit_errors}, CRC={row.crc}"
|
||
)
|
||
|
||
|
||
def main() -> int:
|
||
parser = argparse.ArgumentParser(description=__doc__)
|
||
parser.add_argument("--frame-count", type=int, default=FRAME_COUNT)
|
||
arguments = parser.parse_args()
|
||
result = run_lab046b_validation(arguments.frame_count)
|
||
print("LAB046B SOFTWARE CONTINUOUS IQ VALIDATION")
|
||
print("RF TX = 0")
|
||
print("known TX frame boundaries at RX = NO")
|
||
print(
|
||
f"main IQ = {result.ideal.sample_count} samples, "
|
||
f"{result.ideal.iq_dtype}, {result.ideal.iq_memory_bytes} bytes"
|
||
)
|
||
for scenario in (result.ideal, result.impaired, result.noisy, result.malformed):
|
||
_print_scenario(scenario)
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
raise SystemExit(main())
|